arXiv subjects

Mohammad Khajenejad

Publications and source records attributed to Mohammad Khajenejad.

At least 19 recordsLinked to original sources

arXiv2026

Resilient Interval Observer-Based Control for Cooperative Adaptive Cruise Control under FDI Attack

Connectivity in connected and autonomous vehicles (CAVs) introduces vulnerability to cyber threats such as false data injection (FDI) attacks, which can compromise system reliability and safety. To ensure resilience, this paper proposes a control framework combining a nonlinear controller with an interval observer for robust state estimation under measurement noise. The observer bounds leader's states, while a neural network-based estimator estimates the unknown FDI attacks in real time. These estimates are then used to mitigate FDI attack effects maintaining safe inter-vehicle spacing. The proposed approach leverages an idea of interval observer-based estimation and merges model-based and learning-based methods to achieve accurate estimations and real-time performance. MATLAB/Simulink results confirm resilient tracking, precise FDI attack estimation, and robustness to noise, demonstrating potential for real-world CACC applications under cyberattacks, disturbance, and bounded measurement noise.

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arXiv2025

Linear Program-Based Stability Conditions for Nonlinear Autonomous Systems

This paper introduces a novel approach to evaluating the asymptotic stability of equilibrium points in both continuous-time (CT) and discrete-time (DT) nonlinear autonomous systems. By utilizing indirect Lyapunov methods and linearizing system dynamics through Jacobian matrices, the methodology replaces traditional semi-definite programming (SDP) techniques with computationally efficient linear programming (LP) conditions. This substitution substantially lowers the computational burden, including time and memory usage, particularly for high-dimensional systems. The stability criteria are developed using matrix transformations and leveraging the structural characteristics of the system, improving scalability. Several examples demonstrated the computational efficiency of the proposed approach compared to the existing SDP-based criteria, particularly for high-dimensional systems.

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arXiv2025

Optimal Dynamic Control of Bounded Jacobian Discrete-Time Systems via Interval Observers

This paper presents an optimal dynamic control framework for bounded Jacobian nonlinear discrete-time (DT) systems with nonlinear observations affected by both state and process noise. Rather than directly stabilizing the uncertain system, we focus on stabilizing an interval observer in a higher dimensional space, whose states bound the true system states. Our nonlinear dynamic control method introduces added flexibility over traditional static and linear approaches, effectively compensating for system nonlinearities and enabling potentially tighter closed-loop intervals. Additionally, we establish a separation principle that allows for the design of observer and control gains. We further derive tractable matrix inequalities to ensure system stability in the closed-loop configuration. The simulation results show that the proposed dynamic control approach significantly outperforms a static counterpart method.

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arXiv2025

Robust Output-Feedback MPC for Nonlinear Systems with Applications to Robotic Exploration

This paper introduces a novel method for robust output-feedback model predictive control (MPC) for a class of nonlinear discrete-time systems. We propose a novel interval-valued predictor which, given an initial estimate of the state, produces intervals which are guaranteed to contain the future trajectory of the system. By parameterizing the control input with an initial stabilizing feedback term, we are able to reduce the width of the predicted state intervals compared to existing methods. We demonstrate this through a numerical comparison where we show that our controller performs better in the presence of large amounts of noise. Finally, we present a simulation study of a robot navigation scenario, where we incorporate a time-varying entropy term into the cost function in order to autonomously explore an uncertain area.

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arXiv2025

Computationally Efficient State and Model Estimation via Interval Observers for Partially Unknown Systems

This paper addresses the synthesis of interval observers for partially unknown nonlinear systems subject to bounded noise, aiming to simultaneously estimate system states and learn a model of the unknown dynamics. Our approach leverages Jacobian sign-stable (JSS) decompositions, tight decomposition functions for nonlinear systems, and a data-driven over-approximation framework to construct interval estimates that provably enclose the true augmented states. By recursively computing tight and tractable bounds for the unknown dynamics based on current and past interval framers, we systematically integrate these bounds into the observer design. Additionally, we formulate semi-definite programs (SDP) for observer gain synthesis, ensuring input-to-state stability and optimality of the proposed framework. Finally, simulation results demonstrate the computational efficiency of our approach compared to a method previously proposed by the authors.

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arXiv2024

Optimal Feedback Stabilizing Control of Bounded Jacobian Discrete-Time Systems via Interval Observers

This paper addresses optimal feedback stabilizing control for bounded Jacobian nonlinear discrete-time (DT) systems with nonlinear observations, affected by state and process noise. Instead of directly stabilizing the uncertain system, we propose stabilizing a higher-dimensional interval observer whose states enclose the true system states. Our nonlinear control approach introduces additional flexibility compared to linear methods, compensating for system nonlinearities and allowing potentially tighter closed-loop intervals. We also establish a separation principle, enabling independent design of observer and control gains, and derive tractable linear matrix inequalities, resulting in a stable closed-loop system.

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arXiv2024

Optimal Interval Observers for Bounded Jacobian Nonlinear Dynamical Systems

In this chapter, we introduce two interval observer designs for discrete-time (DT) and continuous-time (CT) nonlinear systems with bounded Jacobians that are affected by bounded uncertainties. Our proposed methods utilize the concepts of mixed-monotone decomposition and embedding systems to design correct-by-construction interval framers, i.e., the interval framers inherently bound the true state of the system without needing any additional constraints. Further, our methods leverage techniques for positive/cooperative systems to guarantee global uniform ultimate boundedness of the framer error, i.e., the proposed interval observer is input-to-state stable. Specifically, our two interval observer designs minimize the $\mathcal{H}_{\infty}$ and $L_1$ gains, respectively, of the associated linear comparison system of the framer error dynamics. Moreover, our designs adopt a multiple-gain observer structure, which offers additional degrees of freedom, along with coordinate transformations that may improve the feasibility of the resulting optimization programs. We will also discuss and propose computationally tractable optimization formulations to compute the observer gains. Finally, we compare the efficacy of the proposed designs against existing DT and CT interval observers.

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arXiv2024

Guaranteed Privacy-Preserving $\mathcal{H}_{\infty}$-Optimal Interval Observer Design for Bounded-Error LTI Systems

This paper furthers current research into the notion of guaranteed privacy, which provides a deterministic characterization of the privacy of output signals of a dynamical system or mechanism. Unlike stochastic differential privacy, guaranteed privacy offers strict bounds on the proximity between the ranges of two sets of estimated data. Our approach relies on synthesizing an interval observer for a perturbed linear time-invariant (LTI) bounded-error system. The design procedure incorporates a bounded noise perturbation factor computation and observer gains synthesis. Consequently, the observer simultaneously provides guaranteed private and stable interval-valued estimates for a desired variable. We demonstrate the optimality of our design by minimizing the $\mathcal{H}_{\infty}$ norm of the observer error system. Furthermore, we assess the accuracy of our proposed mechanism by quantifying the loss incurred when considering guaranteed privacy specifications. Finally, we illustrate the outperformance of the proposed approach to differential privacy through simulations.

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arXiv2024

Tight Remainder-Form Decomposition Functions with Applications to Constrained Reachability and Guaranteed State Estimation

This paper proposes a tractable family of remainder-form mixed-monotone decomposition functions that are useful for over-approximating the image set of nonlinear mappings in reachability and estimation problems. Our approach applies to a new class of nonsmooth, discontinuous nonlinear systems that we call either-sided locally Lipschitz semicontinuous (ELLS) systems, which we show to be a strict superset of locally Lipschitz continuous (LLC) systems, thus expanding the set of systems that are formally known to be mixed-monotone. In addition, we derive lower and upper bounds for the over-approximation error and show that the lower bound is achieved with our proposed approach, i.e., our approach constructs the tightest, tractable remainder-form mixed-monotone decomposition function. Moreover, we introduce a set inversion algorithm that along with the proposed decomposition functions, can be used for constrained reachability analysis and guaranteed state estimation for continuous- and discrete-time systems with bounded noise.

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arXiv2024

Distributed Resilient Interval Observer Synthesis for Nonlinear Discrete-Time Systems

This paper introduces a novel recursive distributed estimation algorithm aimed at synthesizing input and state interval observers for nonlinear bounded-error discrete-time multi-agent systems. The considered systems have sensors and actuators that are susceptible to unknown or adversarial inputs. To solve this problem, we first identify conditions that allow agents to obtain nonlinear bounded-error equations characterizing the input. Then, we propose a distributed interval-valued observer that is guaranteed to contain the disturbance and system states. To do this, we first detail a gain design procedure that uses global problem data to minimize an upper bound on the $\ell_1$ norm of the observer error. We then propose a gain design approach that does not require global information, using only values that are local to each agent. The second method improves on the computational tractability of the first, at the expense of some added conservatism. Further, we discuss some possible ways of extending the results to a broader class of systems. We conclude by demonstrating our observer on two examples. The first is a unicycle system, for which we apply the first gain design method. The second is a 145-bus power system, which showcases the benefits of the second method, due to the first approach being intractable for systems with high dimensional state spaces.

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arXiv2023

Distributed Task Allocation for Self-Interested Agents with Partially Unknown Rewards

This paper provides a novel solution to a task allocation problem, by which a group of agents decides on the assignment of a discrete set of tasks in a distributed manner. In this setting, heterogeneous agents have individual preferences and associated rewards for doing each task; however, these rewards are only known asymptotically. We start by formulating the assignment problem by means of a combinatorial partition game for known rewards, with no constraints on number of tasks per agent. We relax this into a weight game, which together with the former, are shown to contain the optimal task allocation in the corresponding set of Nash Equilibria (NE). We then propose a projected, best-response, ascending gradient dynamics (PBRAG) that converges to a NE in finite time. This forms the basis of a distributed online version that can deal with a converging sequence of rewards by means of an agreement sub-routine. We present simulations that support our results

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arXiv2023

Resilient State Estimation for Nonlinear Discrete-Time Systems via Input and State Interval Observer Synthesis

This paper addresses the problem of resilient state estimation and attack reconstruction for bounded-error nonlinear discrete-time systems with nonlinear observations/ constraints, where both sensors and actuators can be compromised by false data injection attack signals/unknown inputs. By leveraging mixed-monotone decomposition of nonlinear functions, as well as affine parallel outer-approximation of the observation functions, along with introducing auxiliary states to cancel out the effect of the attacks/unknown inputs, our proposed observer recursively computes interval estimates that by construction, contain the true states and unknown inputs of the system. Moreover, we provide several semi-definite programs to synthesize observer gains to ensure input-to-state stability of the proposed observer and optimality of the design in the sense of minimum $\mathcal{H}_{\infty}$ gain.

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arXiv2023

Control of Discrete-Time LTI Systems using Stochastic Ensemble Systems

In this paper, we study the control properties of a new class of stochastic ensemble systems that consists of families of random variables. These random variables provide an increasingly good approximation of an unknown discrete, linear-time invariant (DLTI) system, and can be obtained by a standard, data-driven procedure. Our first result relates the reachability properties of the stochastic ensemble system with that of the limiting DLTI system. We then provide a method to combine the control inputs obtained from the stochastic ensemble systems to compute a control input for the DLTI system. Later, we deal with a particular kind of stochastic ensemble system generated from realizing Bernoulli random variables. For this, we characterize the variance of the computed state and control. We also do the same for a situation where the data is updated sequentially in a streaming fashion. We illustrate the results numerically in various simulation examples.

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arXiv2023

Computing Controlled Invariant Sets of Nonlinear Control-Affine Systems

In this paper, we consider the computation of controlled invariant sets (CIS) of discrete-time nonlinear control affine systems. We propose an iterative refinement procedure based on polytopic inclusion functions, which is able to approximate the maximal controlled invariant set to within a guaranteed precision. In particular, this procedure allows us to guarantee the invariance of the resulting near-maximal CIS while also computing sets of control inputs that enforce the invariance. Further, we propose an accelerated version of this procedure which refines the CIS by computing backward reachable sets of individual components of set unions, rather than all at once. This reduces the total number of iterations required for convergence, especially when compared with existing methods. Finally, we compare our methods to a sampling-based approach and demonstrate the improved accuracy and faster convergence.

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arXiv2023

Distributed Resilient Interval Observers for Bounded-Error LTI Systems Subject to False Data Injection Attacks

This paper proposes a novel distributed interval-valued simultaneous state and input observer for linear time-invariant (LTI) systems that are subject to attacks or unknown inputs injected both on their sensors and actuators. Each agent in the network leverages a singular value decomposition (SVD) based transformation to decompose its observations into two components, one of them unaffected by the attack signal, which helps to obtain local interval estimates of the state and unknown input and then uses intersection to compute the best interval estimate among neighboring nodes. We show that the computed intervals are guaranteed to contain the true state and input trajectories, and we provide conditions under which the observer is stable. Furthermore, we provide a method for designing stabilizing gains that minimize an upper bound on the worst-case steady-state observer error. We demonstrate our algorithm on an IEEE 14-bus power system.

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arXiv2022

Guaranteed Privacy of Distributed Nonconvex Optimization via Mixed-Monotone Functional Perturbations

In this paper, we introduce a new notion of guaranteed privacy that requires that the change of the range of the corresponding inclusion function to the true function is small. In particular, leveraging mixed-monotone inclusion functions, we propose a privacy-preserving mechanism for nonconvex distributed optimization, which is based on deterministic, but unknown, affine perturbation of the local objective functions, which is stronger than probabilistic differential privacy. The design requires a robust optimization method to characterize the best accuracy that can be achieved by an optimal perturbation. Subsequently, this is used to guide the refinement of a guaranteed-private perturbation mechanism that can achieve a quantifiable accuracy via a theoretical upper bound that is shown to be independent of the chosen optimization algorithm.

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arXiv2022

Distributed Interval Observers for Bounded-Error LTI Systems

This paper proposes a novel distributed interval observer design for linear time-invariant (LTI) discrete-time systems subject to bounded disturbances. In the proposed observer algorithm, each agent in a networked group exchanges locally-computed framers or interval-valued state estimates with neighbors, and coordinates its update via an intersection operation. We show that the proposed framers are guaranteed to bound the true state trajectory of the system by construction, i.e., without imposing any additional assumptions or constraints. Moreover, we provide necessary and sufficient conditions for the collective stability of the distributed observer, i.e., to guarantee the uniform boundedness of the observer error sequence. In particular, we show that such conditions can be tractably satisfied through a constructive and distributed approach. Moreover, we provide an algorithm to verify some structural conditions for a given system, which guarantee the existence of the proposed observer. Finally, simulation results demonstrate the effectiveness of our proposed method compared to an existing distributed observer in the literature.

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arXiv2022

$\mathcal{H}_{\infty}$-optimal Interval Observer Synthesis for Uncertain Nonlinear Dynamical Systems via Mixed-Monotone Decompositions

This paper introduces a novel $\mathcal{H}_{\infty}$-optimal interval observer synthesis for bounded-error/uncertain locally Lipschitz nonlinear continuous-time (CT) and discrete-time (DT) systems with noisy nonlinear observations. Specifically, using mixed-monotone decompositions, the proposed observer is correct by construction, i.e., the interval estimates readily frame the true states without additional constraints or procedures. In addition, we provide sufficient conditions for input-to-state (ISS) stability of the proposed observer and for minimizing the $\mathcal{H}_{\infty}$ gain of the framer error system in the form of semi-definite programs (SDPs) with Linear Matrix Inequalities (LMIs) constraints. Finally, we compare the performance of the proposed $\mathcal{H}_{\infty}$-optimal interval observers with some benchmark CT and DT interval observers.

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